We designed our system to overlay automatic face detection with a manual correction pass to lower the amount of labor required. However, straightforward per-frame annotation still was not feasible. For reference, Celeb-A is one of the largest annotated face datasets, with around 200,000 images [1]}. Our dataset of 22 sessions consists of 4.7 million image frames. If we generously assume that automatic face detection marks 90% of frames and that it takes a human annotator one second per frame, annotation would take 130 hours, or 6 hours per session. This ballpark estimate does not consider false positives and the presence of more than one faces. Therefore, it was imperative to design a system that would allow an annotator to quickly make corrections while remaining highly accurate.
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